Florida Polytechnic University

About: Florida Polytechnic University is a education organization based out in Lakeland, Florida, United States. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 302 authors who have published 538 publications receiving 6549 citations. The organization is also known as: Florida Poly.

Data Analysis of the Moxtra Application

Abstract: This paper will explain the importance of a digital forensic analysis and the different sections that it contains. This is to highlight the importance of the information that can be discovered through performing a digital forensic analysis. There is also a description of the Moxtra application to explain what the application is used for and its different capabilities. Following that, the paper will dive into the problem statement that it will research. Then, it will describe the methodology used to solve the problem statement. It will provide details of the test cases and results that were retrieved after performing the cases. Finally, it analysis the results and provides a conclusion.

Leveraging Boids to Implement an Intelligent Swarming Robotic System

Abstract: The proposed project explores the utility of Boids as a basis for building an intelligent robotic platform, capable of exhibiting complex flocking and exploration behaviors. Ideally, this platform would retain sufficient flexibility, such that it may be trivially retooled for a variety of applications. The environment for our proposed platform will also include a simulator and user interface, to allow for easy and rapid testing of new algorithms and coefficients. The interface would also provide functionality that is necessary to view simulated units, as well as to change their parameters.

Compatibility of battery and ultra-capacitor cells equalization for hybrid energy storage system

Abstract: This paper presented the stability of using switched capacitor converter (SCC) topology with cells equalization for battery and ultracapacitor (UC) modules in hybrid energy storage systems (ESS's). The main advantage of using SCCs for Electric vehicle and plug- in hybrid electric vehicle (EV/PHEV) energy management is the absence of transformers and inductors, low electromagnetic interference (EMI), reduce size and cost of energy ESS and simple and easy control strategy.

Potential role of particulate matter in COVID-19 transmission and severity: A case study of Delhi

Abstract: The whole world is going under health, social and economic crisis due to a highly transmittable, pathogenic and invasive pneumococcal novel coronavirus disease (COVID-19). World health organization (WHO) has declared Serve Acute Respiratory Coronavirus 2 (SARS-CoV-2) as a cause for the COVID-19 pandemic. Its uncontrollable and variable impact has opened a global challenge for the research community to cop up with its high inter-individual variability and for policymakers to understand the environmental factors governing its severity. Various aspects identified for its variable spread and severity include demographic, geographic, atmospheric, and economic factors. The geographic and demographic variation in air quality has emerged as the most projecting factor governing COVID-19 transmission and severity. Air contamination induces airway hyper-responsiveness and inflammation, which cause severity in respiratory and cardiovascular diseases, especially in COVID-19. Various air contaminants, including sulfur dioxide, ammonia, particulate matter (PM), oxides of carbon, ozone, and nitrogen oxides, have been reported to play a direct and indirect role in COVID-19 impact. Amongst all, PM plays a direct role in the spread of COVID-19 by acting as a secondary transmission route through aerosols. It also impacts the human immune system through promoting overexpression of angiotensin-converting enzyme 2 (ACE-2) receptors. According to the World Air Quality report 2020, released by IQAir, Delhi is in the world's top ten polluted cities, contributing an average annual PM2.5 concentration of 84.1 micrograms per cubic meter. The population of Delhi has been severely affected by COVID-19 during the initial phase and first wave in 2020. Hence, it is essential to evaluate the association between PM2.5 concentration and COVID-19 impact. With this motivation, we have studied the correlation of PM2.5 concentration with COVID-19 cases and resulting mortalities from June 2020 to December 2020. The data for PM2.5 concentration in the atmosphere has been taken from 38 air quality stations from different parts of Delhi. The correlation has been established through the machine learning approach using Pearson analysis. A data pre-processing technique including null imputation using mean values has been done for data structuring to address missing and random data values. The studies show a moderate correlation of PM2.5 concentration with daily confirmed COVID-19 cases with Pearson's correlation coefficient (r) equal to 0.326 during the first peak period (June 2020 till August 2020). However, a strong correlation with the 'r-value' of 0.598 was observed during the second peak of September 2020 to November 2020. It can be accounted to the dynamic equilibrium amongst two governing factors. They include a rise in the concentration of PM2.5 from September onwards due to meteorological conditions and human activities, and an increase in COVID-19 testing capacity and infrastructure, leading to a rise in the number of reported COVID-19 cases. The significance of the correlation is established through its 'p-value' (p <0.05). Hence, the results suggest a significant correlation between COVID-19 cases and PM2.5 during its peaks. Our findings open a new prospect for policymakers, researchers, and health workers to design appropriate strategies for air quality monitoring to combat COVID-19 transmission and severity and similar infectious diseases.

A Comprehensive COVID-19 Database for the United States

Abstract: The diversity in responses to and conditions resulting from the COVID-19 pandemic in the United States has provided rich data for researchers to study, especially as the pandemic continues to progress. With more than a full year of data available in different regions and at different granularities, methods of analysis requiring larger datasets are now worth examining or refining. Furthermore, as the United States seeks to move away from national and state-wide policies into approaches focused on individual communities, open data must be provided at both the state and county levels. In this paper, a comprehensive database encompassing COVID-19 data and a large body of related data is proposed. The database includes data on cases and deaths, testing, mobility, demographics, weather, and more at both the US state and county levels. The system was implemented using the Python framework Django and the high-performance RDBMS PostgreSQL. A data-processing pipeline was implemented using the asynchronous task library Celery to gather and clean data from various verified sources. This database has been used to build a web application for concise reporting and an open API for public access to the data. A reference web application using the API is currently available at www.bigdatacovid.com, and the API is available at www.bigdatacovid.com/api/v1, with API documentation available on the website.